US EditionSome Viral Videos of Texas Flooding Might Be Fake. Here’s How to Spot Them.
CENTRAL TEXAS, JUL 6 – The false rescue story spread rapidly amid July 4 floods in Central Texas, complicating relief efforts as officials confirmed at least 89 deaths and over 40 missing.
- The Kerr County Lead published a story on July 6 about two girls found alive, clinging to a tree during central Texas flash floods that began July 4 and killed over 100 people.
- The initial report spread due to multiple eyewitness accounts and a viral volunteer video but was rapidly debunked and retracted after Kerr County Sheriff Larry Leitha declared it 100% false.
- The floods triggered widespread misinformation amplified by social media, including compiled videos from unrelated disasters, as creators engaged in 'engagement farming' to boost views during heightened public attention.
- Volunteer Cord Shiflet apologized in a later video saying he shared unverified information from officials and expressed regret for sensationalizing the story amid community hopes for positive news.
- The incident highlights challenges journalists face with verifying information in disasters, underscoring expert calls for skepticism online and the need for newsrooms to review errors after spreading emotionally charged misinformation.
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How a Hopeful and Completely False Story About 2 Girls Rescued While Clinging to Tree in Texas Floods Spread Like Wildfire
A story of girls saved from a tree during the Texas floods turned out to be a "classic tale of misinformation," but that did not stop it from spreading.
·United States
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Texas floods: How to spot fake disaster videos
Numerous social media videos claiming to be about the Texas flash floods misrepresented footage from previous natural disasters. Inauthentic content usually comes with red flags, such as added dramatic sound effects and clips from multiple locations stitched in one video.
·Saint Petersburg, United States
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Total News Sources19
Leaning Left13Leaning Right0Center3Last UpdatedBias Distribution81% Left
Bias Distribution
- 81% of the sources lean Left
81% Left
L 81%
C 19%
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